Thomas Hankemeier

The paper analyzes longitudinal metabolomic changes of biogenic amines in plasma and CSF of TgF344 AD rats and WT controls, revealing sex- and age-specific differences.

This study identifies metabolic targets in breast cancer by analyzing multi-omics data from cell lines, revealing subtype-specific dependencies and potential drug targets.

The paper identifies reduced triacylglycerols and lipid droplets in resilient Alzheimer's donors compared to AD patients, using multi-omics analysis of brain tissue.

The paper analyzes metabolites in AD patients to show inflammation and sEH dysregulation, with sex and disease stage differences, and identifies markers of disease progression and cognitive resilience.

The paper identifies foamy microglia as contributors to disease progression in secondary progressive MS, linking oxylipins to lesion expansion and proposing monoacylglycerol lipase as a therapeutic target.

The paper identifies age, sex, and disease-dependent lipidomic changes in TgF344-AD rats, showing similarities to human lipid profiles and developing predictive models for AD.

XomicsToModel integrates multi-omics data with a generic metabolic model to generate context-specific, thermodynamically consistent metabolic models.

This study identifies metabolic signatures associated with pulmonary embolism in COVID-19 patients using longitudinal ICU data, revealing key lipid and amine changes post-event.

mzQuality is a software tool for quality control in metabolomics data that corrects technical variation and prepares data for analysis.

This study evaluates normalization strategies for lipidomic data in cell line panels, finding that data-based approaches improve replicate similarity but may introduce artifacts.

fluxTrAM automates processing of tracer-based metabolomics data and integrates it with genome-scale metabolic models for flux analysis.

This paper introduces a new method for genome-scale metabolic flux inference using conserved moieties, demonstrating its computational efficiency and accuracy with isotopic labeling data.

The paper identifies a biomarker for in-ovo gender prediction in chicken eggs using mass spectrometry, achieving high sensitivity and specificity for female gender detection.

This study integrates genomics, epigenomics, and metabolomics data to identify ADHD biomarkers and explore omics-level connections, finding associations between PGSs, CpGs, and metabolites.

This study examines heritability of urinary metabolites in children, finding substantial genetic contributions across amines, organic acids, and steroids.

The paper develops genome-scale metabolic models of human dopaminergic neurons to study metabolic dysfunction in Parkinson's disease using exometabolomic and transcriptomic data.

The paper presents STATegra, a multi-omics dataset of mouse B-cell differentiation combining 10 omics technologies to study dynamic cellular processes.

This study estimates heritability and genetic contributions to metabolite levels using genome-wide data and twin-family cohorts, revealing class-specific genetic influences.

The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.

The paper identifies altered bile acid profiles in Alzheimer's disease, linking them to cognitive impairment and genetic variants through a large study.

The paper identifies associations between serum bile acid metabolites and AD biomarkers in the A/T/N framework, suggesting a potential role for bile acid pathways in AD pathophysiology.

The paper describes an automated microfluidic platform for culturing Parkinson’s disease patient-derived dopaminergic neurons and validates its ability to monitor differentiation and neuronal activity.